【摘要】 针对电力系统变电所故障诊断系统中含有大量不确定信息和实时性要求高的特点,以电力系统变电所开关保护信息为基础,基于智能互不融合的思想,将粗糙集、神经网络和专家系统有机结合在一起,提出一种电力系统变电所故障诊断的新方法。首先在数据采集和预处理的基础上,利用混合聚类法对原始故障诊断样本进行离散化处理,然后利用粗糙集理论对样本决策表进行属性约简,删除冗余信息,得到能够覆盖原始数据特征的具有最小条件属性的相应学习样本集。再运用径向基函数(RBF)神经网络对故障诊断知识进行模式识别,并结合专家系统,利用其推理判断能力,对RBF神经网络的某些输出结果进行必要的修正。最后通过故障诊断实例,说明了方法的有效性。更多还原

【Abstract】 In accordance with characteristics of more indeterminate information and higher speed request in power system substation fault diagnosis system,on the basis of switch and relay protecting information of substation,according to the intelligence complementary strategy,a new substation fault diagnosis method based on rough sets-neural network-expert system was presented.Firstly,based on data acquisition and pretreatment,the original fault diagnosis samples were discretized by the hybrid clustering method.Then,the decision attribute was reduced to delete redundant information for obtaining the minimum fault feature subset.In the course of identifying fault diagnosis through radial basis function(RBF)neural network,some output results of RBF neural network was modified by using the inference capability expert system.The results show that the presented method is effective by applying the presented method to the certain substation.更多还原